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talented Machine Learning Engineer with expertise in healthcare data analysis, medical terminology, and cutting-edge machine learning techniques. The ideal candidate will play a crucial role in designing
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: Department of Electrical & Computer Engineering Term: Winter 2026 Course subject code: ECSE 552 Course Title: Deep Learning Course Credits: 4 credits Location: ENGMD 279 Schedule: Monday and Wednesday: 8:35 am
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: Optical Fibre Communications; David Plant ECSE 551: Machine Learning for Engineers; Mark Coats ECSE 552: Deep Learning; Course Lecturer ECSE 554: Applied Robotics; Hsiu-Chin Lin ECSE 597: Circuit Simulators
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area: Reinforcement learning; probabilistic approaches in machine learning; mathematical foundations of machine learning. Previous experience as an instructor at the university-level is required. Related
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: COMP 550- Course Title: Natural Language Processing Course Number: COMP 551- Course Title: Applied Machine Learning Course Number: COMP 555 - Course Title: Information Privacy Course Number: COMP 558
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: Desautels Course Title and Course Number: Enterprise Data Science: Concepts and Algorithms/Enterprise Machine Learning in Production INSY 674/684 Estimated Number of Positions: 1 Total Hours of Work per Term
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initiatives, or with programming language experience and experience in machine learning and health informatics. An Understanding of the digital health space, is expected but not essential. Good Communication
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client service for our executive participants. The virtual training programs are an important component of the MEI learning platform and contribute to the overall MEI revenue. We are looking to extend
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. Learning outcomes: By the end of this course, the students will be able to: Describe the purpose of primary hardware and software components of computer networks. Explain the fundamental concepts of common
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course lecturer position to teach in Winter 2026. Please include your cover letter and Curriculum Vitae and if applicable a copy of your Study/Work Permit. A brief course outline is optional. MECH 289